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Identification of subset-specific dendritic cell progenitors reveals early commitment in the bone marrow [RNA-Seq]

GSE60781 Mus musculus (mouse) Single-cell RNA-seq 251 samples Submitted 2015/06/01 Platform GPL13112
At a glance

Single-cell RNA-seq · mouse · 251 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
251 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Dendritic cells (DCs) are antigen sensing and presenting cells that are essential for effective immunity. Existing as a multi-subset population, divided by distinct developmental and functional characteristics1,2, DC subsets play important and unique roles in responses to pathogens, vaccines and cancer therapies, as well as during immune-pathologies.
Read the full summary ↓
Therefore therapeutic manipulation of the DC compartment is an attractive strategy. However, our incomplete knowledge of the inter-relationship between DC subsets and how they develop from progenitors in the bone marrow (BM) has so far limited the realization of their therapeutic potential. DCs arise from a cascade of progenitors that gradually differentiate in the BM; first, the macrophage DC progenitor (MDP), then common DC progenitor (CDP), and lastly the Pre-DC, which will leave the BM to seed peripheral tissues before differentiating into mature DCs3,4. While the basic outline of this process is known, how subset commitment and development is regulated at the molecular level remains poorly understood. Here we reveal that the Pre-DC population in mice is heterogeneous, containing uncommitted Ly6c+/-Siglec-H+ cells as well as Ly6c+Siglec-H- and Ly6c-Siglec-H- sub-populations that are developmentally fated to become Th2/17-inducing CD11b+ DCs and Th1-inducing CD8α+ DCs, respectively. Using single cell analysis by microfluidic RNA sequencing, we found that DC subset imprinting occurred at the mRNA level from the CDP stage, revealing that subset fate is defined in the BM and not in peripheral tissues. Single cell transcriptome analysis allowed identification of the molecular checkpoints between progenitor stages and revealed new regulators of DC-poiesis, shedding light on the role of cell cycle control and specific transcription factors in DC lineage development. These data advance our knowledge of the steady-state regulation of DC populations and open promising new avenues for investigation of the therapeutic potential of DC subset-specific targeting in vivo to improve vaccine-based and immunotherapeutic strategies.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE60781_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA259535 and SRA study SRP045794. Searching any of these in the dataset finder brings you back here.

Samples in this study

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